Novartis: Accelerating Drug Development with AI-Powered Clinical Trial Transformation

Overview

Novartis partnered with AWS Professional Services and Accenture to modernize their drug development infrastructure and integrate AI across clinical trials, with the goal of reducing trial development cycles by at least six months. The initiative built a GXP-compliant data mesh platform on AWS with Databricks for processing, enabling AI use cases including protocol generation and an intelligent decision system (digital twin). Early results from the patient safety domain showed 72% query speed improvements, 60% storage cost reduction, and 160+ hours of manual work eliminated. The protocol generation use case achieved 83-87% acceleration in producing compliant protocols.

The challenge

Novartis's drug development and clinical trial data was fragmented across heterogeneous sources — file shares, relational databases, life sciences platforms like Veeva Vault, and master data management systems like Reltio — hindering AI adoption across the R&D continuum in an industry where developing new medicines typically takes 15 years. Novartis set the goal of reducing clinical trial development cycles by at least six months.

The solution

Novartis partnered with AWS Professional Services and Accenture to build a next-generation GXP-compliant data platform on AWS with Databricks for processing, following a data mesh architecture with five components: an ingestion framework grouping similar source types, a three-tier data product storage/processing model, data management and governance (catalog, lineage, quality), self-service data consumption via BI tools and AI/ML platforms, and a central observability platform. The platform is designed to support AI use cases including protocol generation and an Intelligent Decision System (digital twin) for simulating clinical trial operational plans; compliance was built in from the design phase using Architectural Decision Records and threat modeling rather than added afterward.

Reported business value

The patient safety domain, an early adopter, built 16 data pipelines processing approximately 17 terabytes of data, yielding a 72% reduction in query speeds, a 60% reduction in storage costs, and 160+ hours of manual work eliminated. The protocol generation use case, which currently runs on demo data or legacy on-premises systems, achieved 83-87% acceleration in producing protocols that meet compliance standards. The six-month clinical trial reduction goal remains to be fully proven, but early results suggest substantive progress toward that target.